Inspired by GAN, Ledig et al. proposed the Super-Resolution Generative Adversarial Network (SRGAN [1]}). In SRGAN, the generator \(G\)  is essentially a SR model that trained to fool the discriminator \(D\) , and \(D\)  is trained to distinguish SR images from HR images. Therefore, the generator can learn to produce outputs that are highly similar to HR images, and then reconstruct more perceptual and natural SR images. Following this approach, the generative loss \(\mathcal {L}_{Gen}(I_x)\)  can be defined as:
\(\mathcal {L}_{Gen}=-\log D_{\theta _D}(G_{\theta _G}(I_x)),\) 
